Simple remote sensing detection of Corymbia calophylla flowers using common 3 –band imaging sensors

Campbell, Tristan; Fearns, Peter

doi:10.1016/j.rsase.2018.04.009

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…Several studies have used spectral mixture analyses to identify photosynthetic and non-photosynthetic fractions [76][77][78] and vegetation indices to estimate concentration of plant pigments [79][80][81]. However, similar efforts are lacking to discriminate flowers from other parts of the plant and existing techniques have been applied mostly to high-resolution aerial or hyperspectral data [82][83][84][85][86]. Notably, the EBI developed by Chen et al (2019) [73] was found to perform poorly in discriminating non-white flowers, and it may be that other band combinations are more suitable for different coloured petals.…”

Section: Vegetation Indices For Phenological Research In Woody Speciesmentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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Section: Vegetation Indices For Phenological Research In Woody Speciesmentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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“…The increasing use of unmanned aerial vehicles (UAVs) has been also used to map, with higher spatial resolution, species of the genus Acacia on savannas ecosystems [48] or in Portugal [15]. Though the use of remote sensing-derived information for the flowering period is a common approach to differentiate the species pattern [49][50][51][52][53], and has been also used to map Acacia in South Africa [40], little is known about the use of phenological periods within a year that experiences previous or later blooming, which may help improve IAP detection. In this sense, the spectral difference between phenology periods within a year, resembling for example the well-known spectral differences linked to pre and post hazard events (e.g., forest harvest or fires) requires further research in a Mediterranean context for IAP continuous monitoring.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Efficacy of Phenological Spectral Differences to Detect Invasive Alien Acacia dealbata Using Sentinel-2 Data in Southern Europe

Domingo

Rodríguez²,

Gómez-García

et al. 2023

Remote Sensing

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Invasive alien plants are transforming the landscapes, threatening the most vulnerable elements of local biodiversity across the globe. The monitoring of invasive species is paramount for minimizing the impact on biodiversity. In this study, we aim to discriminate and identify the spatial extent of Acacia dealbata Link from other species using RGB-NIR Sentinel-2 data based on phenological spectral peak differences. Time series were processed using the Earth Engine platform and random forest importance was used to select the most suitable Sentinel-2 derived metrics. Thereafter, a random forest machine learning algorithm was trained to discriminate between A. dealbata and native species. A flowering period was detected in March and metrics based on the spectral difference between blooming and the pre flowering (January) or post flowering (May) months were highly suitable for A. dealbata discrimination. The best-fitted classification model shows an overall accuracy of 94%, including six Sentinel-2 derived metrics. We find that 55% of A. dealbata presences were widely widespread in patches replacing Pinus pinaster Ait. stands. This invasive alien species also creates continuous monospecific stands representing 33% of the presences. This approach demonstrates its value for detecting and mapping A. dealbata based on RGB-NIR bands and phenological peak differences between blooming and pre or post flowering months providing suitable information for an early detection of invasive species to improve sustainable forest management.

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“…Current approaches of automated flower mapping work with image resolutions in the range of centimeters or even meters per pixel (Abdel-Rahman et al, 2015 ; Landmann et al, 2015 ; Chen et al, 2019 ) and are therefore not suited to detect individual flowers and differentiate between flower species of similar color. Other approaches are tailored to a single species (Horton et al, 2017 ; Campbell and Fearns, 2018 ) and are not applicable to a wide range of use cases.…”

Section: Introductionmentioning

confidence: 99%

Flower Mapping in Grasslands With Drones and Deep Learning

et al. 2022

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Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.

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Simple remote sensing detection of Corymbia calophylla flowers using common 3 –band imaging sensors

Cited by 6 publications

References 14 publications

Status of Phenological Research Using Sentinel-2 Data: A Review

Status of Phenological Research Using Sentinel-2 Data: A Review

Assessing the Efficacy of Phenological Spectral Differences to Detect Invasive Alien Acacia dealbata Using Sentinel-2 Data in Southern Europe

Flower Mapping in Grasslands With Drones and Deep Learning

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